Entry requirements

120 credits including 60 credits in mathematics and/or data science including at least 45 credits in mathematics. Participation in Introduction to Data Science or participation in both Inference Theory II and Regression Analysis. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student should be able to:

• give an account of the philosophy of Bayesian models and their specific model assumptions;
• choose suitable informative and non-informative prior distributions;
• derive posterior distributions;
• apply computer intensive methods for approximating the posterior distribution using R;
• make correct inference from theoretical and approximated posterior distributions;
• be able to interpret the results obtained by Bayesian methods.

Content

Decision theoretic foundations. The minimaxity. The choice of prior distributions. Conjugate families. Bayesian point estimation. Bayesian tests. MCMC. Gibbs sampler. Bayesian model choice. Empirical Bayes extension.

Instruction

Lectures and computer sessions.

Assessment

Written examination at the end of the course. Compulsory assignments during the course.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.